refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-parameter
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title: Parameter
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Model Parameter, Weight, Trainable Parameter]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.95
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verification_status: applied
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tags: [parameter, weight, hyperparameter, ml-fundamentals]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: pytorch
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---
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# Parameter
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## 매 한 줄
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> **"매 learned by data vs set by human"**. Parameter = model 이 training 중 학습 (weight, bias). Hyperparameter = 매 human 이 사전 설정 (lr, depth, batch size). 2026 frontier: 매 trillion-parameter models (GPT-5, Claude Opus 4.7) — 매 scale 의 dominant axis.
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## 매 핵심
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### 매 parameter vs hyperparameter
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- **Parameter (θ)**: 매 trainable, gradient descent 의 update target. Examples: W, b in `y = Wx + b`.
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- **Hyperparameter**: 매 fixed before training, 매 architecture/optim choice. Examples: learning rate, batch size, num_layers, dropout p.
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- 매 distinction 모호 case: prompt token (soft prompt 시 parameter, hard prompt 시 input).
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### 매 parameter types
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- **Weights**: matrix multiply coefficients (`W` in `Wx + b`).
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- **Biases**: additive offsets (`b`).
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- **Embeddings**: lookup table (vocab × dim).
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- **LayerNorm γ, β**: scale/shift learned per channel.
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- **Buffers**: 매 NOT params — running statistics (BatchNorm running_mean), moving averages.
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### 매 modern scale
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- BERT-base (2018): 110M.
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- GPT-3 (2020): 175B.
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- GPT-4 (2023): ~1.7T (rumored MoE).
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- Llama 3.1 405B (2024): 405B dense.
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- GPT-5 / Claude Opus 4.7 (2025-2026): trillion-scale, MoE common.
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- 매 active params (MoE) ≠ total params.
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### 매 응용
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1. Model size estimation (memory budget).
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2. Compute budget (Chinchilla scaling: tokens ≈ 20× params).
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3. Compression (quantization, pruning operate on params).
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4. Fine-tuning scope (full vs PEFT — see [[PEFT (Parameter-Efficient Fine-Tuning)]]).
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## 💻 패턴
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### Count parameters
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```python
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def count_params(model):
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total = sum(p.numel() for p in model.parameters())
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trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
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return total, trainable
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total, trainable = count_params(model)
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print(f"Total: {total/1e9:.2f}B, Trainable: {trainable/1e9:.2f}B")
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```
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### Parameter vs buffer
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```python
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import torch.nn as nn
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class MyLayer(nn.Module):
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def __init__(self):
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super().__init__()
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self.weight = nn.Parameter(torch.randn(10, 10)) # trainable
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self.register_buffer("running_mean", torch.zeros(10)) # NOT trainable
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```
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### Freeze parameters (transfer learning)
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```python
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for p in model.encoder.parameters():
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p.requires_grad = False # frozen
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# Only classifier head trains
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optimizer = torch.optim.Adam(
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[p for p in model.parameters() if p.requires_grad], lr=1e-4
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)
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```
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### Memory estimation
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```python
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def model_memory_gb(model, dtype_bytes=2): # bf16
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n = sum(p.numel() for p in model.parameters())
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weights = n * dtype_bytes
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gradients = n * dtype_bytes # if training
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optimizer = n * 8 # Adam: 2 states × fp32
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return (weights + gradients + optimizer) / 1e9
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print(f"Training memory: {model_memory_gb(model):.1f} GB")
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```
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### Hyperparameter search (Optuna)
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```python
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import optuna
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def objective(trial):
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lr = trial.suggest_float("lr", 1e-5, 1e-2, log=True)
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bs = trial.suggest_categorical("batch_size", [32, 64, 128])
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layers = trial.suggest_int("num_layers", 2, 8)
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return train_and_eval(lr=lr, batch_size=bs, num_layers=layers)
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study = optuna.create_study(direction="maximize")
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study.optimize(objective, n_trials=50)
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```
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### MoE active params
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```python
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# Mixtral 8x7B: 47B total, ~13B active per token (top-2 routing)
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total = 47e9
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experts = 8
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active_per_token = 2
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shared = 13e9 - (47e9 - 13e9*experts) / experts # rough
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Memory budget plan | Total params × dtype × (1 train, 4 with optim) |
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| Inference deployment | Total params × dtype (+ KV cache) |
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| Scaling decision | Chinchilla: tokens ≈ 20 × params |
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| Compute budget | FLOPs ≈ 6 × params × tokens |
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| Fine-tuning | PEFT if params > 1B and 1-GPU |
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**기본값**: 매 always report total + trainable params separately.
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## 🔗 Graph
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- 부모: [[Machine-Learning]]
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- 변형: [[Trainable-Parameter]]
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- 응용: [[LLM_Optimization_and_Deployment_Strategies|Model-Compression]] · [[PEFT (Parameter-Efficient Fine-Tuning)]] · [[LLM_Optimization_and_Deployment_Strategies|Quantization]]
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- Adjacent: [[Scaling-Laws]] · [[MoE]]
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## 🤖 LLM 활용
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**언제**: 매 model size discussion, memory planning, fine-tuning scope decision.
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**언제 X**: 매 high-level user-facing communication (use "model size" instead).
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## ❌ 안티패턴
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- **Confusing param ≠ hyperparam**: 매 calling `lr` a parameter.
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- **Counting frozen as trainable**: 매 reporting 70B "trainable" when only LoRA (0.5%) actually trains.
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- **Ignoring MoE active vs total**: 매 Mixtral 47B treated as 47B compute (실제 13B per token).
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- **Memory underestimation**: 매 forgetting optimizer states (8× param size for Adam fp32).
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## 🧪 검증 / 중복
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- Verified (PyTorch docs, Kaplan 2020 / Hoffmann 2022 scaling laws).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — parameter vs hyperparameter, modern scale, memory math |
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